The present invention relates to data base networks and an improved method for the access of data and, more particularly, to optimization techniques for the flow of data, to improve the efficiency, reliability and quality of service in database networks.
One or more database servers serve a large number of clients. The term “clients” as used herein includes users, application servers and other databases. A schematic system diagram of a prior art database network is shown in FIG. 1.
In the normal operation of a database network, the clients establish a connection with the database server, and send database requests and database updates to the database server. The database server responds to the requests with data and error messages.
Commonly, there is a high processing load, including read/write load and high memory consumption on a database server, caused, for instance, by massive read requests from its clients. At these times, some or all of the clients wait for a long time in response to their data requests, because of limited database resources such as low availability of the database server central processing unit (CPU), low memory availability or a long queue waiting for a response in the database server read/write unit. The term “load” refers herein to any database server resource, typically time and memory and includes response time, read/write time and memory, an execution time and memory, and CPU time and memory.
Another inefficiency in the operation of current database server networks is related to the management of connections. Whenever a new client establishes or breaks a connection, a long database process is generated consuming CPU time, memory and read/write resources. A method which provides efficiently for the pooling of connections between users would increase database network efficiency.
A known cause of inefficiency in database networks is the use of database requests that are not executed using the most optimal execution steps. Inefficient execution of these database requests causes delay in the response to the client, high CPU consumption, high memory consumption or many read/write requests. Although algorithms are available that identify problematic database requests and suggest alternatives, these have not been applied in real time applications.
When several database servers are managed as a cluster, each client connection request is processed, using automatic selection, to one of the database servers in the cluster. Balance between connections is usually achieved when new connections are established, however, when there are several open connections, some of the database servers may become overloaded. These overloaded servers have limited performance and long response times.
Database servers are changed from time to time. When a database server is changed, applications that issue database requests may receive faster or slower response to their requests, and the load per request on the database changes from one version of the database to the other. The requests, that increase load, need to be identified and changed to receive faster response time from the database server and to consume fewer database resources.
There are some prior art applications which have attempted to address at least partially some of these known problems in database network servers.
Disk cache servers are intended to eliminate massive database access by keeping some data in memory. When the database server issues a disk request to retrieve cached data, the data are provided faster from the cache than from the storage medium. However, cache servers generally require that the application be aware of them since they do not guarantee synchronization between the cached data and the database.
There are application servers which provide a partial solution for central pooling of connections. However, they provide application pooling that requires customization of the database application. There are also special purpose applications using internal cache memory in the application.
There is thus a widely recognized need for the optimization of data flow in data base networks, and it would be highly advantageous to have a system and method for the optimization of database access in data base networks, and to improve the efficiency, reliability and quality of service in database networks